Despite its valuable contribution to replanting cost reduction, low-density planting may lead to delayed canopy closure and interfere with tree growth. The effect of planting density on the growth of Sugi (Cryptomeria japonica (L.f.) D.Don) and competing vegetation, and their competitive status was examined in 4 young Sugi plantations with different weeding methods in the Tohoku district of Japan. We measured the height and crown width of the planted Sugi trees, the height of competing vegetation, and their competitive status with Sugi trees. Planting density negatively affected the crown width of Sugi in an 11-year-old stand under complete weeding, with improvement cutting at 10 years old, suggesting competition among planted trees. Despite the negative effect of planting density on the height of competing vegetation, Sugi trees were not covered with competing vegetation in plots with a lower planting density in this stand. However, a low planting density led to a small height and crown width of Sugi, and the large part of them was covered with competing vegetation in 6-7-year-old strip-weeded stands. These results indicate that when low-density planting was used with strip weeding, planted Sugi trees tended to be covered with competing vegetation that thrived in the area between the weeded rows.

To predict future forest road collapses with the increasing influence of climate change, the relationship between the frequency of forest road collapse and rainfall intensity should be determined. Due to the long recurrence period between intense rainfalls, a long-term, broad-based record of forest road collapses is required. The Forest Road Register, which contains the records of the annual number of collapses on forest roads across Japan, is a good data source but does not identify the rainfall intensity that caused each collapse. Thus, we proposed an estimation method using Bayesian inference based on a general regression model to determine rainfall intensity and forest road collapse frequency using Forest Road Register data. To check the feasibility of the proposed method, we evaluated the model using either high- or low-resolution data for forest road collapse in Toyama Prefecture from 1998 to 2018. High resolution data included the date each collapse occurred; thus, the rainfall event that caused each collapse can be identified. Low resolution data included only the annual number of collapses of each road, just like the Forest Road Register does. The expected collapse frequencies were of the same order of magnitude in both models for rainfall events between 100 mm and 400 mm per 24 h. The proposed estimation method using low-resolution data sources is applicable to areas that have a similar or lower frequency of intense rainfall events than Toyama Prefecture. The applicability is also affected by road length registered as one road in the Forest Road Register.